Effective societal responses to rapid climate change in the Arctic rely on an accurate representation of region-specific ecosystem properties and processes. However, this is limited by the scarcity and patchy distribution of field measurements. Here, we use a comprehensive, geo-referenced database of primary field measurements in 1,840 published studies across the Arctic to identify statistically significant spatial biases in field sampling and study citation across this globally important region. We find that 31% of all study citations are derived from sites located within 50 km of just two research sites: Toolik Lake in the USA and Abisko in Sweden. Furthermore, relatively colder, more rapidly warming and sparsely vegetated sites are under-sampled and under-recognized in terms of citations, particularly among microbiology-related studies. The poorly sampled and cited areas, mainly in the Canadian high-Arctic archipelago and the Arctic coastline of Russia, constitute a large fraction of the Arctic ice-free land area. Our results suggest that the current pattern of sampling and citation may bias the scientific consensuses that underpin attempts to accurately predict and effectively mitigate climate change in the region. Further work is required to increase both the quality and quantity of sampling, and incorporate existing literature from poorly cited areas to generate a more representative picture of Arctic climate change and its environmental impacts.
Net primary production (NPP) is the principal source of energy for ecosystems and, by extension, human populations that depend on them. The relationship between the supply and demand of NPP is important for the assessment of socio-ecological vulnerability. We present an analysis of the supply and demand of NPP in the Sahel using NPP estimates from the MODIS sensor and agri-environmental data from FAOSTAT. This synergistic approach allows for a spatially explicit estimation of human impact on ecosystems. We estimated the annual amount of NPP required to derive food, fuel and feed between 2000 and 2010 for 22 countries in sub-Saharan Africa. When comparing annual estimates of supply and demand of NPP, we found that demand increased from 0.44 PgC to 1.13 PgC, representing 19% and 41%, respectively, of available supply due to a 31% increase in the human population between 2000 and 2010. The demand for NPP has been increasing at an annual rate of 2.2% but NPP supply was near-constant with an inter-annual variability of approximately 1.7%. Overall, there were statistically significant (p < 0.05) increases in the NPP of cropland (+6.0%), woodland (+6.1%) and grassland/savanna (+9.4%), and a decrease in the NPP of forests (−0.7%). On the demand side, the largest increase was for food (20.4%) followed by feed (16.7%) and fuel (5.5%). The supplydemand balance of NPP is a potentially important tool from the standpoint of sustainable development, and as an indicator of stresses on the environment stemming from increased consumption of biomass.
Abstract:Field observations of near-surface soil moisture, collected over several seasons in a watershed in suburban Maryland, are compared with values of the topographic soil moisture index generated using digital elevation models (DEMs) at a range of grid cell sizes from photogrammetric and light detection and ranging (LIDAR) data sources. A companion set of near-surface soil moisture observations, DEMs and topographic index values are also presented for a nearby forested catchment. The degree to which topographic index values are an effective indicator of near-surface soil moisture conditions varies in the two environments. The urbanizing environment requires topographic index values from a DEM with a much finer grid cell resolution than the LIDAR data can provide, and the relationship is stronger in wetter conditions. In the forested environment, the DEM resolution required is considerably lower and adequately supported by the photogrammetric data, and the relationship is strong under all moisture conditions. These results provide some insights into the length scales of near-surface hydrological processes in the urbanizing environment, and the resolution of terrain data required to model those processes.
The generation, transport and fate of non-point source pollutants in surface water systems is recognized as a major threat to water supplies, aquatic and coastal ecosystems. The transformation and movement of water, carbon and nutrients through watersheds integrates a set of ecosystem processes along hydrologic flowpaths. Human individual and institutional interactions with these processes involve direct addition or abstraction of these substances, or the alteration of land cover and drainage systems. In natural and developed catchments, these processes often vary at granularities ranging from below the level of a hillslope, up through regional watersheds. This suggests the need for the development of hierarchical analysis tools that can address the integration of a set of biophysical, biogeochemical and socioeconomic processes over a spectrum of scales. We describe and illustrate the use of a watershed model implemented as a spatial object hierarchy, representing successively contained landform classes associated with class specific processes as member functions. The model has been linked in a range of looser and tighter couplings with GRASS and ArcView, supplemented by specific terrain analytical functions. We illustrate the data and model system for an instrumented catchment monitored as part of the Baltimore Ecosystem Study (BES), a Long Term Ecological Research (LTER) site centering on integrated carbon, water and nutrient cycling.
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